Trajectory’s concurrent multi-LoRA stack reports a 2.81× experiment-throughput gain over single-tenant RL, with all code in the NovaSky-AI/SkyRL GitHub repository.
Most language models improve in discontinuous jumps. A team collects data, trains, and ships a new version. This takes months and produces remarkable or catastrophic behavior for users. Trajectory wants to replace that cycle with continual learning.
The Trajectory team published a field report describing how. It built a concurrent, multi-LoRA training platform for continuously learning workloads. The work was done with UC Berkeley Sky Lab and Anyscale. All training code is open-sourced in the NovaSky-AI/SkyRL repository.
The result is a 2.81× end-to-end experiment-throughput improvement. The comparison is against a single-tenant training framework. Trajectory reports no regression on any training rewards.
What Multi-LoRA Training Actually Is
